FI:PA173 Mathematical Morphology - Course Information

PA173 Mathematical Morphology

Extent and Intensity

2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Teacher(s)

doc. RNDr. Petr Matula, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics

Timetable

Mon 12:00–13:50 B411, Mon 14:00–15:50 B311

Prerequisites

Knowledge at the level of course PV131 Digital Image Processing is useful.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-IN)
• Informatics (eng.) (programme FI, D-IN4)
• Informatics (programme FI, D-IN4)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems and Technologies (eng.) (programme FI, D-IN4)
• Computer Systems and Technologies (programme FI, D-IN4)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Social Informatics (programme FI, B-AP)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

Course objectives

At the end of the course students should be able to: understand and explain the principles of mathematical morphology methods and efficient algorithms for their computation; respect their properties and theoretical limits; demonstrate their usage on typical image analysis problems in various application fields; solve image analysis problems using mathematical morphology.

Syllabus

• Structuring element and its decomposition
• Fundamental morphological operators (erosion, dilation, opening, closing, top-hat, ...)
• Hit-or-miss transform, skeletons, thinning, thickening
• Geodesic transformations and metrics
• Morphological reconstructions
• Morphological filters
• Segmentation, watershed transform, markers, hierarchical segmentation
• Efficient implementation of morphological operators
• Granulometry, classification, texture analysis

Literature

• SOILLE, Pierre. Morphological Image Analysis: Principles and Applications. 2nd edition. Berlin: Springer-Verlag, 2003. ISBN 3-540-42988-3. info

Teaching methods

Lectures followed by class exercises in a computer room to gain hands-on experience.

Assessment methods

Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (Autumn 2012, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2012/PA173